A significant challenge of biomedical research is the identification of biological markers (biomarkers) that can be used to predict and better understand the molecular basis of disease. A biomarker is defined as a substance or an event used as an indicator of a biological state that we want objectively to detect, measure and assess as a marker of a normal state, a pathological state (whether for predictive, prognostic or diagnostic purposes), or a pharmacologic response to a therapeutic intervention (a pharmacodynamic marker). The strong need to assess known biomarkers and discover new ones is because even in patients with the same disease altered molecular events in cells are not always similar; tumours, for example, are not the same at the molecular level even if they share common histological features. Thus, there is the need to understand the molecular mechanisms behind the development of diseases, to define specific molecular events, in order to recognize the molecular differences for each patient. In this way, patients can be stratified to receive the most appropriate therapies.
Nowadays, most of the data for pharmacological treatment efficacy or patient prognosis are based on statistical studies. As a result, the accuracy for predicting drug efficacy or prognosis in a single patient depends on the probability that he/she is near to the mean in large cohorts of patients. This approach is based on two approximations: “same tumour histotypes are equally responsive to therapy” and “each individual is pharmacologically equal to another”. It is now widely documented that both the above assumptions are incorrect and there is an increasing need for personalised treatments.
Proteomic approaches attract increasing interest to identify and investigate biomarkers. A major advantage of measuring protein expression instead of their gene transcripts (using for example cDNA arrays) is that there is a variable correlation between mRNA expression levels and protein functional states and it is the latter that typically drive pathogenesis. Moreover, an important advantage of proteomics is the possibility of analysing post-translational modifications which are known to be important biomarkers since they can be altered in many diseases, such as cancer. However, due to the dynamic and complexity of protein networks, the technical difficulties encountered in specifically monitoring such changes in particular proteins are a challenge. Thus, this approach is still fraught with problems. One difficulty is to discover simple non-invasive diagnostic, prognostic and/or predictive methods to detect protein biomarkers and/or their post-translational modifications. It is thus of fundamental importance that the detailed knowledge surrounding molecular pathways responsible for the disease can be monitored in patient samples, such as the concentration of the proteins in these pathways and/or the knowledge of their functional states inside cells.
The most common and easy way to detect a protein is using antibodies, and there are commercially available antibodies that bind phosphorylated proteins. However, these antibodies in several cases are not mono-specific (i.e. recognise modifications on multiple proteins) and they are frequently polyclonal reagents with inherent batch variation issues (monoclonals have been difficult to produce). These issues pose problems when we want to investigate specific modifications such as phosphorylations associated with specific proteins.
Mass Spectroscopy (MS) is a different analytical technique that analyses complex protein samples. It is used to identify proteins, to identify post-translational modifications, to investigate protein interactions and to quantify protein expression. However, standard MS is used following isolation of the proteins from cell lysates or tissue samples and it does not provide any information on cellular localisation. On the other hand, a newer technology called Matrix-Assisted Laser Desorption/Ionization Imaging Mass Spectroscopy (MALDI IMS) combines the sensitivity and specificity of MS with imaging capabilities allowing the investigation of the spatial distribution of proteins, or phospho-proteins or many other biological molecules, from intact cells and tissues. The resolution here is limited to cells and is not subcellular. Furthermore, this approach demands very expensive equipment and is difficult to implement as a standard and routine laboratory procedure. In addition, the methodology is intrinsically limited by sensitivity and coverage—the modifications of choice may not be detected.
Fluorescent Resonance Energy Transfer (FRET) is an alternative coincidence detection technique which involves the transfer of energy from an excited donor fluorophore to an acceptor fluorophore which has an excitation spectrum that overlaps with the donor emission spectrum and is 59 nm away. Based on this principle, one of its applications is to fuse the donor and the acceptor fluorophores separately with two different target molecules. In the circumstances where these two molecules interact with each other, the donor and the acceptor fluorophores will be close enough to generate a FRET signal. FRET biodetectors can be easily introduced into cells and they are well suited for molecular imaging. However, FRET technology also requires a very expensive equipment set-up and substantial analytical resources, which is a problem.
Development of new techniques which require inexpensive technology are still a challenge and there is still a pressing need for high-throughput assays which are required to be inexpensive for standard and routine laboratory procedures.
A different kind of technology is the Duolink in situ Proximity Ligation Assay (PLA). Duolink in situ PLA is from the Swedish company Olink Bioscience. This technology enables detection, visualisation and quantification of individual proteins, protein modifications and protein interactions in tissue and cell samples prepared for microscopy. The assay is described below from an extract taken from the Duolink in situ PLA user manual (http://www.olink.com/). The six steps required are described:                Primary Antibody Step—The sample of interest is incubated with two primary antibodies that bind to the protein/s to be detected. These antibodies have to be from two different species (e.g. mouse monoclonal and rabbit polyclonal).        Secondary Antibody Step—Secondary antibodies are added which have been conjugated with two oligonucleotides named Proximity Ligation Assay probes (PLA probes) MINUS and PLUS.        Hybridisation Step—Two other oligonucleotides are added which will hybridise to the two PLA probes only if they are in close proximity.        Ligation Step—A reaction using ligase will then join the two hybridised oligonucleotides to form a closed circle.        Amplification Step—Polymerase and nucleotides are added and the oligonucleotide arm of one of the PLA probes acts as a primer for a rolling-circle amplification (RCA) reaction using the ligated circle as a template. This generates a concatemeric (repeated sequence) product extending from the oligonucleotide arm of the PLA probe.        Detection Step—Fluorescent labelled oligonucleotides are added which will hybridise to the RCA product. The signal is visible as a distinct fluorescent dot and can be analysed by fluorescent microscopy.        
Thus the duolink technology is a complex multi-stage process. Moreover, duolink requires multiple antibodies from different species. In addition duolink relies on oligonucleotides conjugated to antibodies which can be a difficult and inefficient reaction. Thus duolink exhibits a number of drawbacks as well as being labour intensive and complicated to use.
The present invention seeks to overcome problem(s) associated with the prior art.